Data Science vs Data Analytics

Data Science and Data Analytics are closely interconnected fields that share common tasks such as data analysis, forecasting, and uncovering insights. In many smaller companies, these roles may be combined, with a single individual functioning as both a data scientist and a data analyst. However, despite the overlap, there are distinct differences between these roles, which we will explore in detail below.

1. The roles of data analysts and data scientists are situated at different levels within the project hierarchy.
   • Data analysts typically operate at a higher abstract level. Their responsibilities encompass determining the required data, interpreting it, formulating hypotheses, devising methods to validate these hypotheses, сreating predictions and effectively communicating the final results through data visualizations.
   • Conversely, data scientists work more closely at a lower level. They are directly involved in model creation and evaluation, data processing, as well as writing pipelines and handling ETL (Extract Transform Load) processes.
2. Different technologies are utilized by data analysts and data scientists to carry out their tasks:
   • Data analysts primarily employ a range of statistical methods and visualizations that are user-friendly and do not necessitate significant costs for development and evaluation.
   • On the other hand, data scientists work with a broader spectrum of tools, including machine learning algorithms, neural networks, and diverse scripts and pipelines.
3. They possess distinct educational backgrounds:
   • Data analytics specialists primarily come from mathematical or economics backgrounds.
   • Conversely, data scientists are computer science specialists who are well-versed in statistical analysis, machine learning algorithms, and also possess knowledge in software development, computer networks, and related areas. This multifaceted skill set enables them to effectively integrate algorithms into project structures.